
import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################

__lowerCAmelCase     :	Dict       			=  16
__lowerCAmelCase     :	Union[str, Any]       			=  32

def a__       (			A_,	A_ = 16   ):






  '''simple docstring'''





  __magic_name__								=			AutoTokenizer.from_pretrained("""bert-base-cased"""   )
  __magic_name__								=			load_dataset("""glue""",	"""mrpc"""   )

  def tokenize_function(A_   ):
    # max_length=None => use the model max length (it's actually the default)
    __magic_name__								=			tokenizer(examples["""sentence1"""],	examples["""sentence2"""],	truncation=A_,	max_length=A_   )
    return outputs

  # Apply the method we just defined to all the examples in all the splits of the dataset
  # starting with the main process first:
  with accelerator.main_process_first():
    __magic_name__								=			datasets.map(
        A_,	batched=A_,	remove_columns=["""idx""", """sentence1""", """sentence2"""],	)

  # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
  # transformers library
  __magic_name__								=			tokenized_datasets.rename_column("""label""",	"""labels"""   )

  def collate_fn(A_   ):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    __magic_name__								=			128 if accelerator.distributed_type == DistributedType.TPU else None
    # When using mixed precision we want round multiples of 8/16
    if accelerator.mixed_precision == "fp8":
      __magic_name__								=			16
    elif accelerator.mixed_precision != "no":
      __magic_name__								=			8
    else:
      __magic_name__								=			None

    return tokenizer.pad(
        A_,	padding="""longest""",	max_length=A_,	pad_to_multiple_of=A_,	return_tensors="""pt""",	)

  # Instantiate dataloaders.
  __magic_name__								=			DataLoader(
      tokenized_datasets["""train"""],	shuffle=A_,	collate_fn=A_,	batch_size=A_   )
  __magic_name__								=			DataLoader(
      tokenized_datasets["""validation"""],	shuffle=A_,	collate_fn=A_,	batch_size=A_   )

  return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
    from accelerate.test_utils.training import mocked_dataloaders

    __lowerCAmelCase     :	Union[str, Any]       			=  mocked_dataloaders  # noqa: F811

def a__       (			A_,	A_   ):






  '''simple docstring'''





  if os.environ.get("""TESTING_MOCKED_DATALOADERS""",	A_   ) == "1":
    __magic_name__								=			2
  # Initialize Accelerator

  # New Code #
  # We pass in "all" to `log_with` to grab all available trackers in the environment
  # Note: If using a custom `Tracker` class, should be passed in here such as:
  # >>> log_with = ["all", MyCustomTrackerClassInstance()]
  if args.with_tracking:
    __magic_name__								=			Accelerator(
        cpu=args.cpu,	mixed_precision=args.mixed_precision,	log_with="""all""",	project_dir=args.project_dir   )
  else:
    __magic_name__								=			Accelerator(cpu=args.cpu,	mixed_precision=args.mixed_precision   )
  # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
  __magic_name__								=			config["""lr"""]
  __magic_name__								=			int(config["""num_epochs"""]   )
  __magic_name__								=			int(config["""seed"""]   )
  __magic_name__								=			int(config["""batch_size"""]   )
  set_seed(A_   )

  __magic_name__	,       __magic_name__								=			get_dataloaders(A_,	A_   )
  __magic_name__								=			evaluate.load("""glue""",	"""mrpc"""   )

  # If the batch size is too big we use gradient accumulation
  __magic_name__								=			1
  if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
    __magic_name__								=			batch_size // MAX_GPU_BATCH_SIZE
    __magic_name__								=			MAX_GPU_BATCH_SIZE

  # Instantiate the model (we build the model here so that the seed also control new weights initialization)
  __magic_name__								=			AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""",	return_dict=A_   )

  # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
  # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
  # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
  __magic_name__								=			model.to(accelerator.device   )

  # Instantiate optimizer
  __magic_name__								=			AdamW(params=model.parameters(),	lr=A_   )

  # Instantiate scheduler
  __magic_name__								=			get_linear_schedule_with_warmup(
      optimizer=A_,	num_warmup_steps=100,	num_training_steps=(len(A_   ) * num_epochs) // gradient_accumulation_steps,	)

  # Prepare everything
  # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
  # prepare method.
  __magic_name__	,       __magic_name__	,       __magic_name__	,       __magic_name__	,       __magic_name__								=			accelerator.prepare(
      A_,	A_,	A_,	A_,	A_   )

  # New Code #
  # We need to initialize the trackers we use. Overall configurations can also be stored
  if args.with_tracking:
    __magic_name__								=			os.path.split(A_   )[-1].split("""."""   )[0]
    accelerator.init_trackers(A_,	A_   )

  # Now we train the model
  for epoch in range(A_   ):
    model.train()
    # New Code #
    # For our tracking example, we will log the total loss of each epoch
    if args.with_tracking:
      __magic_name__								=			0
    for step, batch in enumerate(A_   ):
      # We could avoid this line since we set the accelerator with `device_placement=True`.
      batch.to(accelerator.device   )
      __magic_name__								=			model(**A_   )
      __magic_name__								=			outputs.loss
      # New Code #
      if args.with_tracking:
        total_loss += loss.detach().float()
      __magic_name__								=			loss / gradient_accumulation_steps
      accelerator.backward(A_   )
      if step % gradient_accumulation_steps == 0:
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()

    model.eval()
    for step, batch in enumerate(A_   ):
      # We could avoid this line since we set the accelerator with `device_placement=True` (the default).
      batch.to(accelerator.device   )
      with torch.no_grad():
        __magic_name__								=			model(**A_   )
      __magic_name__								=			outputs.logits.argmax(dim=-1   )
      __magic_name__	,       __magic_name__								=			accelerator.gather_for_metrics((predictions, batch["""labels"""])   )
      metric.add_batch(
          predictions=A_,	references=A_,	)

    __magic_name__								=			metric.compute()
    # Use accelerator.print to print only on the main process.
    accelerator.print(f'''epoch {epoch}:''',	A_   )

    # New Code #
    # To actually log, we call `Accelerator.log`
    # The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
    if args.with_tracking:
      accelerator.log(
          {
              """accuracy""": eval_metric["""accuracy"""],
              """f1""": eval_metric["""f1"""],
              """train_loss""": total_loss.item() / len(A_   ),
              """epoch""": epoch,
          },	step=A_,	)

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
  if args.with_tracking:
    accelerator.end_training()

def a__       (			):






  '''simple docstring'''





  __magic_name__								=			argparse.ArgumentParser(description="""Simple example of training script."""   )
  parser.add_argument(
      """--mixed_precision""",	type=A_,	default=A_,	choices=["""no""", """fp16""", """bf16""", """fp8"""],	help="""Whether to use mixed precision. Choose"""
      """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
      """and an Nvidia Ampere GPU.""",	)
  parser.add_argument("""--cpu""",	action="""store_true""",	help="""If passed, will train on the CPU."""   )
  parser.add_argument(
      """--with_tracking""",	action="""store_true""",	help="""Whether to load in all available experiment trackers from the environment and use them for logging.""",	)
  parser.add_argument(
      """--project_dir""",	type=A_,	default="""logs""",	help="""Location on where to store experiment tracking logs` and relevent project information""",	)
  __magic_name__								=			parser.parse_args()
  __magic_name__								=			{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
  training_function(A_,	A_   )


if __name__ == "__main__":
    main()



import html

from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends


if is_bsa_available():
    import bsa
    from bsa import BeautifulSoup


__lowerCAmelCase     :	int       			=  logging.get_logger(__name__)




class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  def __init__(					self     :					Union[str, Any]     ,				**UpperCamelCase__     :					Union[str, Any]       )     ->      Union[str, Any]:







    """simple docstring"""






    requires_backends(self     ,				["""bs4"""]       )
    super().__init__(**UpperCamelCase__       )
  def 				_lowercase				(					self     :					Dict     ,				UpperCamelCase__     :					Union[str, Any]       )     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			[]
    __magic_name__								=			[]
    __magic_name__								=			element if element.name else element.parent
    for parent in child.parents:  # type: bs4.element.Tag
      __magic_name__								=			parent.find_all(child.name     ,				recursive=UpperCamelCase__       )
      xpath_tags.append(child.name       )
      xpath_subscripts.append(
          0 if 1 == len(UpperCamelCase__       ) else next(i for i, s in enumerate(UpperCamelCase__     ,				1       ) if s is child       )       )
      __magic_name__								=			parent
    xpath_tags.reverse()
    xpath_subscripts.reverse()
    return xpath_tags, xpath_subscripts
  def 				_lowercase				(					self     :					Union[str, Any]     ,				UpperCamelCase__     :					List[Any]       )     ->      str:







    """simple docstring"""






    __magic_name__								=			BeautifulSoup(UpperCamelCase__     ,				"""html.parser"""       )

    __magic_name__								=			[]
    __magic_name__								=			[]
    __magic_name__								=			[]

    for element in html_code.descendants:
      if type(UpperCamelCase__       ) == bsa.element.NavigableString:
        if type(element.parent       ) != bsa.element.Tag:
          continue

        __magic_name__								=			html.unescape(UpperCamelCase__       ).strip()
        if not text_in_this_tag:
          continue

        all_doc_strings.append(UpperCamelCase__       )

        __magic_name__	,       __magic_name__								=			self.xpath_soup(UpperCamelCase__       )
        stringaxtag_seq.append(UpperCamelCase__       )
        stringaxsubs_seq.append(UpperCamelCase__       )

    if len(UpperCamelCase__       ) != len(UpperCamelCase__       ):
      raise ValueError("""Number of doc strings and xtags does not correspond"""       )
    if len(UpperCamelCase__       ) != len(UpperCamelCase__       ):
      raise ValueError("""Number of doc strings and xsubs does not correspond"""       )

    return all_doc_strings, stringaxtag_seq, stringaxsubs_seq
  def 				_lowercase				(					self     :					List[Any]     ,				UpperCamelCase__     :					Dict     ,				UpperCamelCase__     :					List[Any]       )     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			""""""
    for tagname, subs in zip(UpperCamelCase__     ,				UpperCamelCase__       ):
      xpath += F'''/{tagname}'''
      if subs != 0:
        xpath += F'''[{subs}]'''
    return xpath





  def __call__(					self     :					str     ,				UpperCamelCase__     :					Tuple       )     ->      BatchFeature:







    """simple docstring"""






    __magic_name__								=			False

    # Check that strings has a valid type
    if isinstance(UpperCamelCase__     ,				UpperCamelCase__       ):
      __magic_name__								=			True
    elif isinstance(UpperCamelCase__     ,				(list, tuple)       ):
      if len(UpperCamelCase__       ) == 0 or isinstance(html_strings[0]     ,				UpperCamelCase__       ):
        __magic_name__								=			True

    if not valid_strings:
      raise ValueError(
          """HTML strings must of type `str`, `List[str]` (batch of examples), """
          F'''but is of type {type(UpperCamelCase__       )}.'''       )

    __magic_name__								=			bool(isinstance(UpperCamelCase__     ,				(list, tuple)       ) and (isinstance(html_strings[0]     ,				UpperCamelCase__       ))       )

    if not is_batched:
      __magic_name__								=			[html_strings]

    # Get nodes + xpaths
    __magic_name__								=			[]
    __magic_name__								=			[]
    for html_string in html_strings:
      __magic_name__	,       __magic_name__	,       __magic_name__								=			self.get_three_from_single(UpperCamelCase__       )
      nodes.append(UpperCamelCase__       )
      __magic_name__								=			[]
      for node, tag_list, sub_list in zip(UpperCamelCase__     ,				UpperCamelCase__     ,				UpperCamelCase__       ):
        __magic_name__								=			self.construct_xpath(UpperCamelCase__     ,				UpperCamelCase__       )
        xpath_strings.append(UpperCamelCase__       )
      xpaths.append(UpperCamelCase__       )

    # return as Dict
    __magic_name__								=			{"""nodes""": nodes, """xpaths""": xpaths}
    __magic_name__								=			BatchFeature(data=UpperCamelCase__     ,				tensor_type=UpperCamelCase__       )

    return encoded_inputs

